Background: Primary cicatricial alopecia (PCA) is a group of disorders causing irreversible hair loss because of hair follicle destruction. Although bacterial colonization is suspected to contribute to PCA pathogenesis, its role remains unclear. Objective: To investigate bacterial colonization patterns and antibiotic susceptibility profiles in patients with PCA compared to those with non-inflammatory scalp conditions. Methods: This retrospective study analyzed bacterial cultures from scalp swabs of 161 subjects (68 patients with PCA and 93 controls) at a tertiary hospital between June 2011 and December 2023. Bacterial species and antibiotic resistance rates were evaluated using subgroup analyses of neutrophilic PCA (NCA). Results: PCA cultures showed a higher prevalence of Staphylococcus aureus (24.3%) and S. lugdunensis (8.1%) than controls, where S. capitis (54.5%) was predominant. Gram-negative bacteria were more frequent in the PCA group (13.5%) than in the control group (9.9%), with Klebsiella spp.(10.9%) being the most prevalent. Resistance rates were significantly higher in PCA for benzylpenicillin, fusidic acid, erythromycin, clindamycin, oxacillin, and telithromycin (p<0.05). Methicillin-resistant S. aureus was identified in 15% of S. aureus isolates from NCA cases. Gram-negative bacteria in PCA also exhibited increased resistance to ampicillin and ampicillin/sulbactam. Conclusion PCA exhibits distinct bacterial colonization and elevated antibiotic resistance, particularly in the neutrophilic subtypes. Bacterial culture and susceptibility testing are essential for targeted therapies in clinical practice. Further multicenter microbiome analyses with mechanistic studies are needed to elucidate bacterial contributions to PCA pathogenesis.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits. Methods: This study retrospectively analyzed data from the Korean Network for Organ Sharing database, including 1,599 lung transplant candidates between September 2009 and December 2020. We developed the MaxBenefit LAS, combining a waitlist mortality model and a post-transplant survival model using elastic-net Cox regression, was assessed using area under the curve (AUC) values and Uno’s C-index. Its performance was compared to the US LAS in an independent cohort. Results: The waitlist mortality model showed strong predictive performance with AUC values of 0.834 and 0.818 in the training and validation cohorts, respectively. The post-transplant survival model also demonstrated good predictive ability (AUC: 0.708 and 0.685). The MaxBenefit LAS effectively stratified patients by risk, with higher scores correlating with increased waitlist mortality and decreased post-transplant mortality. The MaxBenefit LAS outperformed the conventional LAS in predicting waitlist death and identifying candidates with higher transplant benefits. Conclusion The MaxBenefit LAS offers a promising approach to optimizing lung allocation by balancing the urgency of candidates with their likelihood of survival post-transplant. This novel system has the potential to improve outcomes for lung transplant recipients and reduce waitlist mortality, providing a more equitable allocation of donor lungs.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Purpose: This study aimed to evaluate the outcomes and current status of reduced-port laparoscopic distal gastrectomy (RLDG) compared with multiport laparoscopic distal gastrectomy (MLDG) based on a 2019 nationwide survey of surgical gastric cancer treatments by the Korean Gastric Cancer Association (KGCA). Materials and Methods: The study was conducted retrospectively from March to December 2020 using data from the 2019 KGCA nationwide survey database. To compare RLDG and MLDG based on age, sex, body mass index, American Society of Anesthesiologists score, histological type, tumor invasion, and lymph node metastasis, propensity score matching was performed. Results: Of the 14,076 registered patients with gastric cancer, the five-port approach was the most favored for multiport gastrectomy, accounting for 6,396 (70.9%) cases, followed by the four-port approach, with 1,462 (16.2%) cases. The single-port approach was used in 303 (3.4%) cases, the two-port approach in 95 (1.1%) cases, and the three-port approach in 731 (8.1%) cases. RLDG was performed in 805 patients (6.4%), MLDG was conducted in 4,831 patients (34.3%), and 804 patients were 1:1 matched in each group. The average operation time was shorter in the RLDG (168.2±49.1 min vs. 179.5±61.5 min, P<0.001). No significant difference was found in blood loss (84.8±115.9 cc vs. 75.5±119.6 cc, P=0.152), overall complication rates (11.3% vs. 13.1%, P=0.254), or complications ≥ to grade IIIa (3.2% vs. 4.4%, P=0.240). Conclusions This study revealed that RLDG is a safe and effective surgical option for gastric cancer with the potential to offer shorter operation times without increasing the risk of complications.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

We describe the updated Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of cervical cancer, version 5.1. The KSGO announced the fifth version of its clinical practice guidelines for the management of cervical cancer in March 2024. The selection of the key questions and the systematic reviews were based on data available up to December 2022. Between 2023 and 2024, substantial findings from large-scale clinical trials and new advancements in cervical cancer research remarkably emerged. Therefore, based on the existing version 5.0, we updated the guidelines with newly accumulated clinical data and added 4 new key questions reflecting the latest insights in the field of cervical cancer. For each question, recommendation was formulated with corresponding level of evidence and grade of recommendation, all established through expert consensus.

Background: Recombinant human follicle-stimulating hormone (rhFSH) is commonly used to treat female infertility, but its short half-life necessitates multiple doses. Even corifollitropin alfa, with an extended half-life, requires supplementary injections of rhFSH after 7 days. This study aimed to develop and evaluate a long-acting follicle-stimulating hormone (FSH) formulation using anti-serum albumin Fab-associated (SAFA) technology to avoid additional injections and enhance ovarian function. Methods: SAFA-FSH was synthesized using a Chinese hamster ovary expression system. Its biological efficacy was confirmed through assays measuring its ability to stimulate cyclic adenosine monophosphate (cAMP) production, estradiol synthesis, and the expression of human cytochrome P450 family 19 subfamily A member 1 (hCYP19α1) and human steroidogenic acute regulatory protein (hSTAR) in human ovarian granulosa (KGN) cells. To evaluate the effects of SAFA-FSH, we compared its impact on serum estradiol levels and ovarian weight increase with that of rhFSH in Sprague-Dawley (SD) rats using the modified Steelman-Pohley test. Results: The results indicated that SAFA-FSH induces cAMP synthesis in KGN cells and upregulates the expression of hCYP19α1 and hSTAR in a dose-dependent manner. Female SD rats, aged 21 days, receiving daily subcutaneous human chorionic gonadotropin injections for 5 days exhibited a significant increase in serum estradiol levels and ovarian weight when administered SAFA-FSH on the first day or when given nine injections of rhFSH over 5 days. Notably, the group receiving SAFA-FSH on the first and third days demonstrated an even greater rise in serum estradiol levels and ovarian weight. Conclusion These findings suggest that SAFA-FSH presents a promising alternative to current rhFSH treatments for female infertility. However, further research is essential to thoroughly assess its safety and efficacy in clinical contexts.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.